{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Installing the Packge\n",
    "This notebook introduces the basics of using Bandits.jl package.\n",
    "\n",
    "First install `MAB.jl` as below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Pkg.add( \"MAB.jl\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Usage\n",
    "To start using the package,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "using MAB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The package is divide into 3 sub modules:\n",
    "* Algorithms\n",
    "* Arms\n",
    "* Experiments\n",
    "\n",
    "`Algorithms` includes all the available algorithms. `Arms` includes the available arm model. `Experiments` is supposed to include code for running experiments and is a work under progress."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining a bandit\n",
    "For this demo, first we will create a bandit with 5 Bernoulli arms. It is not necessary to create a bandit with arms specified as below to use algorithms, but these kind of bandits can be used to benchmark new algorithms.\n",
    "A bandit is an array of arms and can be defined as below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5-element Array{MAB.Arms.Bernoulli,1}:\n",
       " MAB.Arms.Bernoulli(Distributions.Bernoulli{Float64}(p=0.3))\n",
       " MAB.Arms.Bernoulli(Distributions.Bernoulli{Float64}(p=0.4))\n",
       " MAB.Arms.Bernoulli(Distributions.Bernoulli{Float64}(p=0.5))\n",
       " MAB.Arms.Bernoulli(Distributions.Bernoulli{Float64}(p=0.6))\n",
       " MAB.Arms.Bernoulli(Distributions.Bernoulli{Float64}(p=0.7))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bandit = [ Arms.Bernoulli(0.30),\n",
    "            Arms.Bernoulli(0.40),\n",
    "            Arms.Bernoulli(0.50),\n",
    "            Arms.Bernoulli(0.60),\n",
    "            Arms.Bernoulli(0.70)  ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the number of arms of the bandit as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_of_arms = length( bandit )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have a 5-arm Bernoulli Bandit.\n",
    "\n",
    "Each arm has 3 functions associated with it:\n",
    "* `pull!()` - To pull an arm. Returns a reward associated with the arm. pull!() also changes the internal state of the arm if it is a Markovian/Non-stationary arms.\n",
    "* `tick!()` - To change the internal state of a Markovian/Non-stationary arm. Not necessarily return something. May return junk depending on the internal state.\n",
    "* `reset!()` - To reset the internal state of a Markovian/Non-stationary arm.\n",
    "\n",
    "To pull an 1st arm,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Arms.pull!( bandit[1] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above line will randomly return 0/1 according to the underlying probability distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Algorithms\n",
    "Every algorithm has specific initializers which depends on the parameters for the algorithms. Usually the first parameter of the initialization is the number of arms of the bandit. As an example, an instance of $\\epsilon$-Greedy algorithm with $\\epsilon = 0.10$ can be created as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Algorithm: ϵ-Greedy (ϵ = 0.100)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    noOfArms        : 5\n",
      "    noOfSteps       : 0\n",
      "    lastPlayedArm   : 0\n",
      "    ϵ               : 0.1\n",
      "    cummReward      : [0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "    count           : [0, 0, 0, 0, 0]\n",
      "    avgValue        : [0.0, 0.0, 0.0, 0.0, 0.0]"
     ]
    }
   ],
   "source": [
    "alg1 = epsGreedy( no_of_arms, 0.10 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's run an experiment of $750$ timesteps and average it over $1000$ runs to get an average behaviour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "750"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noOfRounds    = 1000\n",
    "noOfTimesteps = 750"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create an array to hold the results of each round of play."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "observations  = zeros( noOfTimesteps, noOfRounds );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can run the $\\epsilon$-Greedy algorithm over this bandit as below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for _round = 1:noOfRounds\n",
    "    reset!( alg1 )\n",
    "    for arm ∈ bandit\n",
    "        Arms.reset!( arm )\n",
    "    end\n",
    "    for _n = 1:noOfTimesteps\n",
    "        armToPull = get_arm_index( alg1 )\n",
    "        reward    = Arms.pull!( bandit[armToPull] )\n",
    "        update_reward!( alg1, reward )\n",
    "        observations[_n,_round] = reward\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above code runs the algorithm for 250 time steps and save the obtained rewards into `observations`. Note that we need to `reset!()` the algorithm and arms between each rounds.\n",
    "\n",
    "To see the average behaviour, we can plot the average reward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x127878850>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgReward = mean( observations, 2 )\n",
    "\n",
    "using PyPlot\n",
    "plot( 1:noOfTimesteps, avgReward, label = Algorithms.info_str(alg1) )\n",
    "legend()\n",
    "grid()\n",
    "PyPlot.ylabel( \"Avg. Reward\" )\n",
    "PyPlot.xlabel( \"Timesteps\" );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congrats!! You have successfully completed the basics of Bandits.jl package. You can to the documentation page of the package to explore available algorithms and arm models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Multiple Algorithms\n",
    "You can also compare performance of multiple algorithms easily with the package. First we'll look into the actual code for doing it. Later we will look into the `Experiments` section to automate this function.\n",
    "\n",
    "As above, we'll start with defining a bandit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bandit1 = [ Arms.Bernoulli(0.25), Arms.Bernoulli(0.35), Arms.Bernoulli(0.45),\n",
    "            Arms.Bernoulli(0.55), Arms.Bernoulli(0.65), Arms.Bernoulli(0.75) ]\n",
    "\n",
    "no_of_arms = length( bandit1 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can define an array of algorithms which we want to test along the associated parameters as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_algs = [ epsGreedy( no_of_arms, 0.10),\n",
    "              epsGreedy( no_of_arms, 0.20),\n",
    "              UCB1( no_of_arms ),\n",
    "              TS( no_of_arms )    ];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can run the experiment as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x11515de90>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = figure()\n",
    "\n",
    "for _alg ∈ test_algs\n",
    "    observations = zeros( noOfTimesteps, noOfRounds )\n",
    "    for _r = 1:noOfRounds\n",
    "        Algorithms.reset!( _alg )\n",
    "        for _arm ∈ bandit1\n",
    "            Arms.reset!( _arm )\n",
    "        end\n",
    "        for _n = 1:noOfTimesteps\n",
    "            armToPull = Algorithms.get_arm_index( _alg )\n",
    "            reward    = Arms.pull!( bandit1[armToPull] )\n",
    "            Algorithms.update_reward!( _alg, reward )\n",
    "            \n",
    "            observations[_n,_r] = reward\n",
    "        end\n",
    "    end\n",
    "    avgReward = mean( observations, 2 );\n",
    "    plot( 1:noOfTimesteps, avgReward, label = Algorithms.info_str(_alg) )\n",
    "end\n",
    "ylabel( \"Avg. Reward\" )\n",
    "xlabel( \"Timesteps\" )\n",
    "title( \"Comparison Plot (Averaged over $noOfRounds runs)\" )\n",
    "ax = gca()\n",
    "ax[:set_ylim]( [0.00, 1.00] )\n",
    "legend()\n",
    "grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You explore the above code by changing the bandit model and the algorithms to compare."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.6.0",
   "language": "julia",
   "name": "julia-0.6"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
